






"""simple docstring"""
import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest



class        UpperCamelCase__ (				a_):







    """simple docstring"""
    __UpperCAmelCase           = (PNDMScheduler,)
    __UpperCAmelCase           = (("""num_inference_steps""", 50),)







    def 				a__			(		self :	Optional[Any]				,    **UpperCamelCase_ :	Union[str, Any] ):





           '''simple docstring'''

           __magic_name__      				=    {
               'num_train_timesteps': 1_0_0_0,
               'beta_start': 0.0_001,
               'beta_end': 0.02,
               'beta_schedule': 'linear',
           }

           config.update(**UpperCamelCase_ )
           return config







    def 				a__			(		self :	List[Any]				,    UpperCamelCase_ :	Dict=0				,    **UpperCamelCase_ :	Dict ):





           '''simple docstring'''

           __magic_name__      				=    dict(self.forward_default_kwargs )
           __magic_name__      				=    kwargs.pop('num_inference_steps'				,    UpperCamelCase_ )
           __magic_name__      				=    self.dummy_sample
           __magic_name__      				=    0.1 * sample
           __magic_name__      				=    [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

           for scheduler_class in self.scheduler_classes:
                  __magic_name__      				=    self.get_scheduler_config(**UpperCamelCase_ )
                  __magic_name__      				=    scheduler_class(**UpperCamelCase_ )
                  scheduler.set_timesteps(UpperCamelCase_ )
                  # copy over dummy past residuals
                  __magic_name__      				=    dummy_past_residuals[:]

                  with tempfile.TemporaryDirectory() as tmpdirname:
                         scheduler.save_config(UpperCamelCase_ )
                         __magic_name__      				=    scheduler_class.from_pretrained(UpperCamelCase_ )
                         new_scheduler.set_timesteps(UpperCamelCase_ )
                         # copy over dummy past residuals
                         __magic_name__      				=    dummy_past_residuals[:]

                  __magic_name__      				=    scheduler.step_prk(UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_				,    **UpperCamelCase_ ).prev_sample
                  __magic_name__      				=    new_scheduler.step_prk(UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_				,    **UpperCamelCase_ ).prev_sample

                  assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"

                  __magic_name__      				=    scheduler.step_plms(UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_				,    **UpperCamelCase_ ).prev_sample
                  __magic_name__      				=    new_scheduler.step_plms(UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_				,    **UpperCamelCase_ ).prev_sample

                  assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"







    def 				a__			(		self :	Union[str, Any] ):





           '''simple docstring'''

           pass







    def 				a__			(		self :	int				,    UpperCamelCase_ :	Optional[int]=0				,    **UpperCamelCase_ :	Dict ):





           '''simple docstring'''

           __magic_name__      				=    dict(self.forward_default_kwargs )
           __magic_name__      				=    kwargs.pop('num_inference_steps'				,    UpperCamelCase_ )
           __magic_name__      				=    self.dummy_sample
           __magic_name__      				=    0.1 * sample
           __magic_name__      				=    [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

           for scheduler_class in self.scheduler_classes:
                  __magic_name__      				=    self.get_scheduler_config()
                  __magic_name__      				=    scheduler_class(**UpperCamelCase_ )
                  scheduler.set_timesteps(UpperCamelCase_ )

                  # copy over dummy past residuals (must be after setting timesteps)
                  __magic_name__      				=    dummy_past_residuals[:]

                  with tempfile.TemporaryDirectory() as tmpdirname:
                         scheduler.save_config(UpperCamelCase_ )
                         __magic_name__      				=    scheduler_class.from_pretrained(UpperCamelCase_ )
                         # copy over dummy past residuals
                         new_scheduler.set_timesteps(UpperCamelCase_ )

                         # copy over dummy past residual (must be after setting timesteps)
                         __magic_name__      				=    dummy_past_residuals[:]

                  __magic_name__      				=    scheduler.step_prk(UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_				,    **UpperCamelCase_ ).prev_sample
                  __magic_name__      				=    new_scheduler.step_prk(UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_				,    **UpperCamelCase_ ).prev_sample

                  assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"

                  __magic_name__      				=    scheduler.step_plms(UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_				,    **UpperCamelCase_ ).prev_sample
                  __magic_name__      				=    new_scheduler.step_plms(UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_				,    **UpperCamelCase_ ).prev_sample

                  assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"







    def 				a__			(		self :	List[str]				,    **UpperCamelCase_ :	List[str] ):





           '''simple docstring'''

           __magic_name__      				=    self.scheduler_classes[0]
           __magic_name__      				=    self.get_scheduler_config(**UpperCamelCase_ )
           __magic_name__      				=    scheduler_class(**UpperCamelCase_ )

           __magic_name__      				=    1_0
           __magic_name__      				=    self.dummy_model()
           __magic_name__      				=    self.dummy_sample_deter
           scheduler.set_timesteps(UpperCamelCase_ )

           for i, t in enumerate(scheduler.prk_timesteps ):
                  __magic_name__      				=    model(UpperCamelCase_				,    UpperCamelCase_ )
                  __magic_name__      				=    scheduler.step_prk(UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_ ).prev_sample

           for i, t in enumerate(scheduler.plms_timesteps ):
                  __magic_name__      				=    model(UpperCamelCase_				,    UpperCamelCase_ )
                  __magic_name__      				=    scheduler.step_plms(UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_ ).prev_sample

           return sample







    def 				a__			(		self :	Optional[Any] ):





           '''simple docstring'''

           __magic_name__      				=    dict(self.forward_default_kwargs )

           __magic_name__      				=    kwargs.pop('num_inference_steps'				,    UpperCamelCase_ )

           for scheduler_class in self.scheduler_classes:
                  __magic_name__      				=    self.get_scheduler_config()
                  __magic_name__      				=    scheduler_class(**UpperCamelCase_ )

                  __magic_name__      				=    self.dummy_sample
                  __magic_name__      				=    0.1 * sample

                  if num_inference_steps is not None and hasattr(UpperCamelCase_				,    'set_timesteps' ):
                         scheduler.set_timesteps(UpperCamelCase_ )
                  elif num_inference_steps is not None and not hasattr(UpperCamelCase_				,    'set_timesteps' ):
                         __magic_name__      				=    num_inference_steps

                  # copy over dummy past residuals (must be done after set_timesteps)
                  __magic_name__      				=    [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
                  __magic_name__      				=    dummy_past_residuals[:]

                  __magic_name__      				=    scheduler.step_prk(UpperCamelCase_				,    0				,    UpperCamelCase_				,    **UpperCamelCase_ ).prev_sample
                  __magic_name__      				=    scheduler.step_prk(UpperCamelCase_				,    1				,    UpperCamelCase_				,    **UpperCamelCase_ ).prev_sample

                  self.assertEqual(output_a.shape				,    sample.shape )
                  self.assertEqual(output_a.shape				,    output_a.shape )

                  __magic_name__      				=    scheduler.step_plms(UpperCamelCase_				,    0				,    UpperCamelCase_				,    **UpperCamelCase_ ).prev_sample
                  __magic_name__      				=    scheduler.step_plms(UpperCamelCase_				,    1				,    UpperCamelCase_				,    **UpperCamelCase_ ).prev_sample

                  self.assertEqual(output_a.shape				,    sample.shape )
                  self.assertEqual(output_a.shape				,    output_a.shape )







    def 				a__			(		self :	Any ):





           '''simple docstring'''

           for timesteps in [1_0_0, 1_0_0_0]:
                  self.check_over_configs(num_train_timesteps=UpperCamelCase_ )







    def 				a__			(		self :	Union[str, Any] ):





           '''simple docstring'''

           for steps_offset in [0, 1]:
                  self.check_over_configs(steps_offset=UpperCamelCase_ )

           __magic_name__      				=    self.scheduler_classes[0]
           __magic_name__      				=    self.get_scheduler_config(steps_offset=1 )
           __magic_name__      				=    scheduler_class(**UpperCamelCase_ )
           scheduler.set_timesteps(1_0 )
           assert torch.equal(
               scheduler.timesteps				,    torch.LongTensor(
                   [9_0_1, 8_5_1, 8_5_1, 8_0_1, 8_0_1, 7_5_1, 7_5_1, 7_0_1, 7_0_1, 6_5_1, 6_5_1, 6_0_1, 6_0_1, 5_0_1, 4_0_1, 3_0_1, 2_0_1, 1_0_1, 1] )				,    )







    def 				a__			(		self :	Optional[int] ):





           '''simple docstring'''

           for beta_start, beta_end in zip([0.0_001, 0.001]				,    [0.002, 0.02] ):
                  self.check_over_configs(beta_start=UpperCamelCase_				,    beta_end=UpperCamelCase_ )







    def 				a__			(		self :	Optional[Any] ):





           '''simple docstring'''

           for schedule in ["linear", "squaredcos_cap_v2"]:
                  self.check_over_configs(beta_schedule=UpperCamelCase_ )







    def 				a__			(		self :	Optional[int] ):





           '''simple docstring'''

           for prediction_type in ["epsilon", "v_prediction"]:
                  self.check_over_configs(prediction_type=UpperCamelCase_ )







    def 				a__			(		self :	List[Any] ):





           '''simple docstring'''

           for t in [1, 5, 1_0]:
                  self.check_over_forward(time_step=UpperCamelCase_ )







    def 				a__			(		self :	Any ):





           '''simple docstring'''

           for t, num_inference_steps in zip([1, 5, 1_0]				,    [1_0, 5_0, 1_0_0] ):
                  self.check_over_forward(num_inference_steps=UpperCamelCase_ )







    def 				a__			(		self :	Optional[int] ):





           '''simple docstring'''

           __magic_name__      				=    2_7

           for scheduler_class in self.scheduler_classes:
                  __magic_name__      				=    self.dummy_sample
                  __magic_name__      				=    0.1 * sample

                  __magic_name__      				=    self.get_scheduler_config()
                  __magic_name__      				=    scheduler_class(**UpperCamelCase_ )

                  scheduler.set_timesteps(UpperCamelCase_ )

                  # before power of 3 fix, would error on first step, so we only need to do two
                  for i, t in enumerate(scheduler.prk_timesteps[:2] ):
                         __magic_name__      				=    scheduler.step_prk(UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_ ).prev_sample







    def 				a__			(		self :	Any ):





           '''simple docstring'''

           with self.assertRaises(UpperCamelCase_ ):
                  __magic_name__      				=    self.scheduler_classes[0]
                  __magic_name__      				=    self.get_scheduler_config()
                  __magic_name__      				=    scheduler_class(**UpperCamelCase_ )

                  scheduler.step_plms(self.dummy_sample				,    1				,    self.dummy_sample ).prev_sample







    def 				a__			(		self :	int ):





           '''simple docstring'''

           __magic_name__      				=    self.full_loop()
           __magic_name__      				=    torch.sum(torch.abs(UpperCamelCase_ ) )
           __magic_name__      				=    torch.mean(torch.abs(UpperCamelCase_ ) )

           assert abs(result_sum.item() - 198.1_318 ) < 1e-2
           assert abs(result_mean.item() - 0.2_580 ) < 1e-3







    def 				a__			(		self :	Any ):





           '''simple docstring'''

           __magic_name__      				=    self.full_loop(prediction_type='v_prediction' )
           __magic_name__      				=    torch.sum(torch.abs(UpperCamelCase_ ) )
           __magic_name__      				=    torch.mean(torch.abs(UpperCamelCase_ ) )

           assert abs(result_sum.item() - 67.3_986 ) < 1e-2
           assert abs(result_mean.item() - 0.0_878 ) < 1e-3







    def 				a__			(		self :	List[Any] ):





           '''simple docstring'''

           __magic_name__      				=    self.full_loop(set_alpha_to_one=UpperCamelCase_				,    beta_start=0.01 )
           __magic_name__      				=    torch.sum(torch.abs(UpperCamelCase_ ) )
           __magic_name__      				=    torch.mean(torch.abs(UpperCamelCase_ ) )

           assert abs(result_sum.item() - 230.0_399 ) < 1e-2
           assert abs(result_mean.item() - 0.2_995 ) < 1e-3







    def 				a__			(		self :	int ):





           '''simple docstring'''

           __magic_name__      				=    self.full_loop(set_alpha_to_one=UpperCamelCase_				,    beta_start=0.01 )
           __magic_name__      				=    torch.sum(torch.abs(UpperCamelCase_ ) )
           __magic_name__      				=    torch.mean(torch.abs(UpperCamelCase_ ) )

           assert abs(result_sum.item() - 186.9_482 ) < 1e-2
           assert abs(result_mean.item() - 0.2_434 ) < 1e-3







"""simple docstring"""
import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mobilebert import MobileBertTokenizer


snake_case : Optional[Any]     		=		logging.get_logger(__name__)

snake_case : str     		=		{"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

snake_case : List[Any]     		=		{
    """vocab_file""": {"""mobilebert-uncased""": """https://huggingface.co/google/mobilebert-uncased/resolve/main/vocab.txt"""},
    """tokenizer_file""": {
        """mobilebert-uncased""": """https://huggingface.co/google/mobilebert-uncased/resolve/main/tokenizer.json"""
    },
}

snake_case : Tuple     		=		{"""mobilebert-uncased""": 5_1_2}


snake_case : List[str]     		=		{}



class        UpperCamelCase__ (				a_):







    """simple docstring"""
    __UpperCAmelCase           = VOCAB_FILES_NAMES
    __UpperCAmelCase           = PRETRAINED_VOCAB_FILES_MAP
    __UpperCAmelCase           = PRETRAINED_INIT_CONFIGURATION
    __UpperCAmelCase           = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __UpperCAmelCase           = MobileBertTokenizer







    def __init__(		self :	Optional[int]				,    UpperCamelCase_ :	Optional[int]=None				,    UpperCamelCase_ :	str=None				,    UpperCamelCase_ :	Tuple=True				,    UpperCamelCase_ :	Any="[UNK]"				,    UpperCamelCase_ :	str="[SEP]"				,    UpperCamelCase_ :	Any="[PAD]"				,    UpperCamelCase_ :	Dict="[CLS]"				,    UpperCamelCase_ :	str="[MASK]"				,    UpperCamelCase_ :	Union[str, Any]=True				,    UpperCamelCase_ :	Dict=None				,    **UpperCamelCase_ :	Dict				,    ):





           '''simple docstring'''

           super().__init__(
               UpperCamelCase_				,    tokenizer_file=UpperCamelCase_				,    do_lower_case=UpperCamelCase_				,    unk_token=UpperCamelCase_				,    sep_token=UpperCamelCase_				,    pad_token=UpperCamelCase_				,    cls_token=UpperCamelCase_				,    mask_token=UpperCamelCase_				,    tokenize_chinese_chars=UpperCamelCase_				,    strip_accents=UpperCamelCase_				,    **UpperCamelCase_				,    )

           __magic_name__      				=    json.loads(self.backend_tokenizer.normalizer.__getstate__() )
           if (
               normalizer_state.get('lowercase'				,    UpperCamelCase_ ) != do_lower_case
               or normalizer_state.get('strip_accents'				,    UpperCamelCase_ ) != strip_accents
               or normalizer_state.get('handle_chinese_chars'				,    UpperCamelCase_ ) != tokenize_chinese_chars
           ):
                  __magic_name__      				=    getattr(UpperCamelCase_				,    normalizer_state.pop('type' ) )
                  __magic_name__      				=    do_lower_case
                  __magic_name__      				=    strip_accents
                  __magic_name__      				=    tokenize_chinese_chars
                  __magic_name__      				=    normalizer_class(**UpperCamelCase_ )

           __magic_name__      				=    do_lower_case







    def 				a__			(		self :	Dict				,    UpperCamelCase_ :	List[str]				,    UpperCamelCase_ :	List[Any]=None ):





           '''simple docstring'''

           __magic_name__      				=    [self.cls_token_id] + token_ids_a + [self.sep_token_id]

           if token_ids_a:
                  output += token_ids_a + [self.sep_token_id]

           return output







    def 				a__			(		self :	List[Any]				,    UpperCamelCase_ :	List[int]				,    UpperCamelCase_ :	Optional[List[int]] = None ):





           '''simple docstring'''

           __magic_name__      				=    [self.sep_token_id]
           __magic_name__      				=    [self.cls_token_id]
           if token_ids_a is None:
                  return len(cls + token_ids_a + sep ) * [0]
           return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]







    def 				a__			(		self :	List[str]				,    UpperCamelCase_ :	str				,    UpperCamelCase_ :	Optional[str] = None ):





           '''simple docstring'''

           __magic_name__      				=    self._tokenizer.model.save(UpperCamelCase_				,    name=UpperCamelCase_ )
           return tuple(UpperCamelCase_ )